Infrastructure, such as dams, weirs and culverts, disrupt the longitudinal connectivity of rivers, causing adverse impacts on fish and other species. This compromises the ability of river ecosystems to provide a range of services that contribute to human well-being. Improving fish passage at artificial barriers is an economic river restoration policy option that can improve the delivery of river ecosystem services provision. Whilst a number of methodologies exist to cost-effectively prioritize barriers for mitigation action, there is also now considerable interest in estimating the economic benefits of increased ecosystem service provision from investing in this activity. This is relevant in a number of policy contexts, including the Water Framework Directive in the EU. This thesis presents a novel bio-economic model that addresses the dual problem of prescribing cost optimal river barrier mitigation solutions whilst, simultaneously, estimating the social benefit of undertaking this activity. Minimal cost solutions are obtained for the problem of barrier mitigation decisions using a mixed integer linear program (MILP). The benefit from marginal improvements in river connectivity and fish species responses is then estimated using the Choice Experiment method. Incorporating these benefit estimates into the MILP generates the final bio-economic model. The specific advantage of this approach is it can readily inform cost benefit analysis of river barrier mitigation policy. The methods are demonstrated using the River Wey in South East England, containing over 650 artificial barriers, as a case study. For the case study, the benefits of investing in river barrier mitigation exceed costs at all budget levels, with the most socially efficient level of investment identified as approximately £30M.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:646956 |
| Date | January 2015 |
| Creators | King, Steven |
| Contributors | O'Hanley, Jesse; Fraser, Iain |
| Publisher | University of Kent |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://kar.kent.ac.uk/48582/ |
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